Given that alcohol affects many organ systems, we performed a cross-tissue and cross-phenotypic analysis of genome wide methylomic variation in AUD using samples from independent cohorts involving post-mortem brain, blood, liver tissue, and various clinical and neuroimaging phenotypes with the goal of identifying disease-associated methylomic DNA variations. Results show that the gene encoding the enzyme Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) was the primary target of epigenetic changes relevant to AUD across data sets (Lohoff et al, 2017). PCSK9 is predominantly expressed in the liver where it is synthesized and secreted. It targets low-density lipoprotein cholesterol receptors (LDL-R) in the liver cells and interferes with the regulation of LDL cholesterol (LDL-C) in the blood. Epigenetic regulation of PCSK9 expression by alcohol consumption is one potential mechanism to explain lipid metabolism abnormalities found in patients with heavy alcohol use. We have now expanded on research related to PCSK9 and have initiated several collaborations with Dr. L. Vendruscolo, Dr. G. Koob, Dr. B. Gao, and Dr. P. Pacher. The collaborations have resulted in the development of a novel animal model of liquid alcohol exposure that more closely resembles alcohol use in humans. In addition, various molecular characterizations have been conducted and are ongoing. Several human clinical studies are being developed. The top brain expressed candidate gene identified by our EWAS scan was Phosphatidylinositol-4-Phosphate 5-Kinase (PIP5K1C). To investigate this target, we conducted genetic as well as animal model work on PIP5K1C. PIP5K1C genetic variants were associated with AUD; and, in an animal model, alcohol exposure lead to upregulation of PIP5K1C in the thalamus and basolateral amygdala (Lee et al, 2018). In another study, we investigated whether methylation in the promoter region of the dopamine transporter (DAT) would have effects on reward processing. Individuals with AUD and healthy controls (HC) completed a monetary incentive delay task in a 3-Tesla magnetic resonance imaging (MRI). DNA was then analyzed for methylation differences. Results showed differences in nucleus accumbens activation during reward processing (Muench et al, 2018b) and support the notion that DNA methylation can affect complex neuronal phenotypes. Next we collaborated with Dr. Nora Volkow to test DAT blood methylation and effects of Positron Emission Tomography (PET) and 11-Ccocaine in individuals with ADHD and HC. The degree of methylation in the promoter region of DAT correlated negatively with DAT availability in caudate in ADHD participants only. DAT gene expression in substantia nigra further correlated positively with DAT protein expression in caudate; however, the sample size of the cohort with ADHD was insufficient to investigate DAT1 and DAT expression levels. Overall, these findings suggest that peripheral DAT promoter methylation may be predictive of striatal DAT availability in adults with ADHD (Wiers et al, 2018). Another interest of CGET is to investigate the molecular mechanisms of negative affective states and how they contribute to the addiction cycle. Negative emotional states contribute to worsening of the addiction cycle and increase risk for relapse (Ahmed and Koob, 1998; George et al, 2014; Koob, 2015). The withdrawal/negative affect stage in humans is often characterized by symptoms of chronic irritability, physical pain, emotional pain, malaise, dysphoria, anhedonia, hopelessness, and loss of motivation for natural rewards. It is hypothesized that the brain attempts to overcome this state by activating the stress-response system (Koob and Le Moal, 2008), as evidenced by dysregulation of the HPA axis and CRF by all major drugs of abuse. In addition, there is strong evidence that anxiety-like responses occur that are modulated by CRF (Koob, 2015). Not much is known with regard to emotional cognitive processing during this addiction stage, and limited data exist regarding emotional learning as a consequence of negative reinforcement. Brain regions involved in negative emotion processing and learning tend to be located, and perhaps overlap, in the medial prefrontal cortex (mPFC), as shown in previous neuroimaging studies (Lissek et al, 2014; Lohoff et al, 2014; Mickey et al, 2011; Phelps et al, 2004). One hypothesis is that individuals, with a history of trauma/early life stress (ELS), might have a harder time unlearning negative emotional states, including low moods and anxiety. Thus, alcohol provides temporary relief initially. However, as the addiction cycle progresses, individuals experience more negative emotions related to withdrawal that they cannot unlearn and thus continue drinking to alleviate negative emotions despite negative consequences. Better understanding the underlying neurocircuitries and molecular mechanisms of negative emotion processing associated with alcohol addiction, including genetic and epigenetic contributors, is crucial for the development of novel interventions and effective prevention strategies. To investigate this, our sections human clinical protocol 15-AA-0127: (Epi)Genetic modulators of fear extinction in alcohol dependence was developed and is actively recruiting. This protocol aims to investigate underlying neurobiology and neurocircuitries of fear extinction in individuals with AUD with and without ELS. In further dissecting the shared mechanisms between negative emotion processing and risk for AUD, we investigated genetic risk variants for depression and AUD. Given that AUD frequently co-occurs with major depressive disorder (MDD), we investigated possible shared genetic susceptibility variants. A recent large-scale GWAS of MDD identified a locus in the TMEM161B-MEF2C region (rs10514299) as a novel risk variant; however, the biological relevance of this variant has not yet been studied. Given previous reports of disrupted reward processing in both AUD and MDD, we hypothesized that rs10514299 would be associated with differences in striatal BOLD responses during reward/loss anticipation in AUD. Our data show for the first time that the previously identified MDD risk variant rs10514299 in TMEM161B-MEF2C predicts neuronal correlates of reward processing in an AUD phenotype, possibly explaining part of the shared pathophysiology and comorbidity between the disorders (Muench et al, 2018a).